Collaborative navigation systems allow two or more navigation systems to work together to produce improved navigation solutions. For example, many autonomous unmanned aerial vehicles can operate and navigate in an environment in the application of surveillance, remote sensing, or rescue. In a collaborative navigation system, these vehicles can cooperate to improve navigation by sharing information.
Communication between two platforms is available sometime during the collaborative navigation procedure. The connections can be established by any measurement which allows the two vehicles to make estimates of the same state. Examples include ranging radio between the two moving platforms, which allow each unit to estimate the positions of both, or using the shared Simultaneous Localization and Mapping (SLAM) landmark states between the two platforms.
A single vehicle integrated navigation system includes an inertial measurement unit and aiding sensors. For example, the inertial measurement unit uses strap-down sensors mechanized as a strap-down unit. The aiding sensors may include a global position system (GPS) receiver, image based sensors, Doppler velocity sensors, or other sensors. A single vehicle integrated navigation system combines the data from the navigation states (such as position, velocity, and altitude) generated by the dynamic plant with the independent aiding sensor data in a Kalman filter algorithm. For example, the navigation algorithm can be based on the Extended Kalman Filter (EKF) which provides, under certain assumptions, a consistent way to deal with the uncertainties associated with the movement and measurement processes.
In collaborative navigation, the vehicle uses information from its own platform sensor, as well as that from other platforms, to enhance a navigation solution of all the platforms. The SLAM method is one way for the navigation, that is, the information obtained by a sensor on a mobile platform is processed to obtain an estimate of its own position while building a map of the environment.
Previous methods for collaborative navigation have used a global centralized filter that includes all the vehicles' navigation states, the dynamic model for each vehicle, and the landmarks (when using SLAM techniques). The disadvantage of a global centralized filter is that each system must maintain estimates of all the states of all the systems. With large numbers of systems collaborating, this becomes computationally impractical. Centralized approaches are also more vulnerable to communication disruptions.